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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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GWideCodeML: A Python Package for Testing Evolutionary Hypotheses at the Genome-Wide Level.

Laura G Macías1,2, Eladio Barrio1,2, Christina Toft3

  • 1Departament de Genètica, Universitat de València, Burjassot, Valencia, Spain.

G3 (Bethesda, Md.)
|October 23, 2020
PubMed
Summary
This summary is machine-generated.

GWideCodeML enables genome-wide positive selection detection using codeml, overcoming gene-by-gene limitations. This Python package analyzes more genes by accommodating variable taxa and pruning topologies for efficient phylogenetic analysis.

Keywords:
Comparative genomicsGenome analysisMolecular evolutionPositive selectionProtein sequence analysisPython

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Area of Science:

  • Molecular Evolution
  • Bioinformatics
  • Computational Biology

Background:

  • The codeml program in PAML is widely used for detecting molecular positive selection.
  • Current methods are limited to gene-by-gene analysis, restricting genome-wide studies.
  • Existing approaches often require one-to-one orthologs, limiting the scope of analysis.

Purpose of the Study:

  • To develop a scalable solution for genome-wide positive selection detection.
  • To overcome the limitations of the gene-by-gene approach of codeml.
  • To enhance the analysis of molecular evolution across diverse genomic datasets.

Main Methods:

  • Introduction of GWideCodeML, a novel Python package.
  • Implementation of parallelized genome-wide codeml analysis.
  • Automated pruning of phylogenetic topologies to fit alignments with variable taxa.

Main Results:

  • GWideCodeML facilitates efficient genome-wide detection of positive selection.
  • The package maximizes the number of analyzed genes by handling variable taxa.
  • Automated topology pruning ensures compatibility with diverse genomic alignments.

Conclusions:

  • GWideCodeML significantly advances genome-wide evolutionary analyses.
  • The package overcomes previous limitations in scale and scope for positive selection studies.
  • This tool enables more comprehensive molecular evolution research.